<![CDATA[The productivity of rice plants in Cirebon Regency varies in each sub-district, which causes an imbalance in rice production. This study aims to group sub-districts in Cirebon Regency based on rice crop productivity using the K-Means Clustering algorithm to support strategic decision-making in the agricultural sector. The research methods applied include Knowledge Discovery in Databases (KDD), which provides data selection, preprocessing, transformation, analysis using K-Means, and evaluation using the Davies-Bouldin Index (DBI). The data used is rice productivity in 2023 from 40 sub-districts, which includes planting area, harvest area, and production yield. The analysis showed that the DBI value was optimal at k=3, with three productivity categories: high, medium, and low. Compared to other methods, the K-Means algorithm has proven to be efficient and accurate in grouping data. This research contributes to local governments in formulating policies to increase rice productivity in areas that require further intervention. These findings also provide a basis for further study by comparing other algorithms to improve the accuracy of the results.]]>
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